This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This research will model digital biodynamic principles of stem cell differentiation within a self organizing parabiotic construct. The algorithm development and code optimization are platform independent. Modeling dynamically differentiable processes within a self organizing construct will require a super computing environment. The model is based on an object oriented and multithreaded programming architecture. Once instantiated an object of class "Stem_Cell" follows known human cell cycle kinetic principles and may then instantiate other daughter objects of this class. Once instantiated each object generates class Threads. The object's threads 1) sense remote data feeds;2) monitor progression through the parabiotic cell cycle;3) identify and categorize other instances of related objects;4) differentiate functionally based on real time parameters. All instances of a given class of differentiating objects may be updated with process variables simultaneously and in real time using a class-notification ("Borg") algorithm. This result will be a Generationally Organized Latent Evolutional Matrix ("Golem") accessible through an internet based Parabiotic system console. Applications include modeling of Stem cell differentiation;self organizing and heuristic systems research, progressively adaptive neuromic matrix development for decision and system control interfaces.